Fri Jan 18 11:32:21 CET 2013Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social WebjunCEUR Workshop ProceedingsEvaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike4672009algorithms citedBy:doerfel2012leveraging collaborative evaluation filtering Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. Collaborative filtering with temporal dynamicshttp://puma.uni-kassel.de/bibtex/2dad3f9050f58acf0551924e537e84e45/jaeschkejaeschke2012-12-10T09:23:31+01:00cf collaborative dynamics filtering netflix stair temporal <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yehuda Koren" itemprop="url" href="/author/Yehuda%20Koren"><span itemprop="name">Y. Koren</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining</span>, </em></span><em>Seite <span itemprop="pagination">447--456</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)

Mon Dec 10 09:23:31 CET 2012New York, NY, USAProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining447--456Collaborative filtering with temporal dynamics2009cf collaborative dynamics filtering netflix stair temporal Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.Referral Web: combining social networks and collaborative filteringhttp://puma.uni-kassel.de/bibtex/2832d16a8c86e769c7ac9ace5381f757e/jaeschkejaeschke2012-12-05T16:50:59+01:00collaborative filtering hybrid network recommender social stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Henry Kautz" itemprop="url" href="/author/Henry%20Kautz"><span itemprop="name">H. Kautz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bart Selman" itemprop="url" href="/author/Bart%20Selman"><span itemprop="name">B. Selman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mehul Shah" itemprop="url" href="/author/Mehul%20Shah"><span itemprop="name">M. Shah</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Communications of the ACM</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">40 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">63--65</span></em> </span>(<em><span>März 1997<meta content="März 1997" itemprop="datePublished"/></span></em>)

Tue Jun 26 10:37:21 CEST 2012ACM RecSys'09 Workshop on Recommender Systems and the Social Weboct17--24CEUR-WS.orgImproving Folkrank With Item-Based Collaborative Filtering5322009bookmarking collaborative filtering folkrank recommender social tagging ﻿Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULikehttp://puma.uni-kassel.de/bibtex/242773258c36ccf2f59749991518d1784/jaeschkejaeschke2012-03-10T14:29:45+01:00collaborative filtering folksonomy item recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Denis Parra" itemprop="url" href="/author/Denis%20Parra"><span itemprop="name">D. Parra</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peter Brusilovsky" itemprop="url" href="/author/Peter%20Brusilovsky"><span itemprop="name">P. Brusilovsky</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web</span>, </em></span><em>Volume 467 von CEUR Workshop Proceedings, </em>(<em><span>Juni 2009<meta content="Juni 2009" itemprop="datePublished"/></span></em>)

Sat Mar 10 14:29:45 CET 2012Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social WebjunCEUR Workshop ProceedingsEvaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike4672009collaborative filtering folksonomy item recommender social tagging Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. Scalable Collaborative Filtering Approaches for Large Recommender Systemshttp://puma.uni-kassel.de/bibtex/21f1be967aed57e6e42a5d99ca98584cd/jaeschkejaeschke2011-12-12T09:02:50+01:00collaborative filtering recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gábor Takács" itemprop="url" href="/author/G%c3%a1bor%20Tak%c3%a1cs"><span itemprop="name">G. Takács</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="István Pilászy" itemprop="url" href="/author/Istv%c3%a1n%20Pil%c3%a1szy"><span itemprop="name">I. Pilászy</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bottyán Németh" itemprop="url" href="/author/Botty%c3%a1n%20N%c3%a9meth"><span itemprop="name">B. Németh</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Domonkos Tikk" itemprop="url" href="/author/Domonkos%20Tikk"><span itemprop="name">D. Tikk</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of Machine Learning Research</em></span></span> </span>(<em><span>Juni 2009<meta content="Juni 2009" itemprop="datePublished"/></span></em>)

Mon Dec 12 09:02:50 CET 2011Journal of Machine Learning Researchjun623--656Scalable Collaborative Filtering Approaches for Large Recommender Systems102009collaborative filtering recommender The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.Tag Recommendations in Folksonomieshttp://puma.uni-kassel.de/bibtex/271bc9f8ae1a53632dc9a2b98b017f152/itegiteg2011-11-22T10:26:32+01:002007 bookmarking collaborative filtering folksonomy itegpub l3s myown recommender social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jaeschke" itemprop="url" href="/author/Robert%20Jaeschke"><span itemprop="name">R. Jaeschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leandro Marinho" itemprop="url" href="/author/Leandro%20Marinho"><span itemprop="name">L. Marinho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lars Schmidt-Thieme" itemprop="url" href="/author/Lars%20Schmidt-Thieme"><span itemprop="name">L. Schmidt-Thieme</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)</span>, </em></span><em>Seite <span itemprop="pagination">13-20</span>. </em><em><span itemprop="publisher">Martin-Luther-Universität Halle-Wittenberg</span>, </em>(<em><span>September 2007<meta content="September 2007" itemprop="datePublished"/></span></em>)

Tue Nov 17 11:02:14 CET 2009New York, NY, USACSCW '00: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work241--250Explaining collaborative filtering recommendations2000cf collaborative explaining filtering recommender Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.Explaining collaborative filtering recommendationsItem-based collaborative filtering recommendation algorithmshttp://puma.uni-kassel.de/bibtex/2a6461157c8102d34b8001c7d33a42684/jaeschkejaeschke2009-05-25T18:22:28+02:00collaborative filtering recommender stair tag <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Badrul Sarwar" itemprop="url" href="/author/Badrul%20Sarwar"><span itemprop="name">B. Sarwar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="George Karypis" itemprop="url" href="/author/George%20Karypis"><span itemprop="name">G. Karypis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joseph Konstan" itemprop="url" href="/author/Joseph%20Konstan"><span itemprop="name">J. Konstan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John Riedl" itemprop="url" href="/author/John%20Riedl"><span itemprop="name">J. Riedl</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">WWW &#039;01: Proceedings of the 10th International Conference on World Wide Web</span>, </em></span><em>Seite <span itemprop="pagination">285--295</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)

Mon May 25 18:22:28 CEST 2009New York, NY, USAWWW '01: Proceedings of the 10th International Conference on World Wide Web285--295Item-based collaborative filtering recommendation algorithms2001collaborative filtering recommender stair tag Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different item-based recommendation generation algorithms. We look into different techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and different techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available user-based algorithms.Collaborative Filtering Recommender Systemshttp://puma.uni-kassel.de/bibtex/21c611c2e32fb3b735c3adcd413e95201/jaeschkejaeschke2008-12-19T15:12:21+01:00cf collaborative filtering recommender webzu <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Ben Schafer" itemprop="url" href="/author/J.%20Ben%20Schafer"><span itemprop="name">J. Schafer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dan Frankowski" itemprop="url" href="/author/Dan%20Frankowski"><span itemprop="name">D. Frankowski</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jon Herlocker" itemprop="url" href="/author/Jon%20Herlocker"><span itemprop="name">J. Herlocker</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shilad Sen" itemprop="url" href="/author/Shilad%20Sen"><span itemprop="name">S. Sen</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Adaptive Web: Methods and Strategies of Web Personalization</span>, </em><em>Volume 4321 von Lecture Notes in Computer Science, </em><em>Kapitel 9, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin, Heidelberg, </em></span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)

Fri Dec 19 15:12:21 CET 2008Berlin, HeidelbergThe Adaptive Web: Methods and Strategies of Web Personalization9291-324Lecture Notes in Computer ScienceCollaborative Filtering Recommender Systems43212007cf collaborative filtering recommender webzu One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.